Spline interpolation uses stats::spline() to interpolate between existing vertices using piecewise cubic polynomials. The coordinates are interpolated independently. The curve will always pass through the vertices of the original feature.

smooth_spline(x, wrap = FALSE, vertex_factor = 5, n)



numeric matrix; matrix of coordinates.


logical; whether the coordinates should be wrapped at the ends, as for polygons and closed lines, to ensure a smooth edge.


double; the proportional increase in the number of vertices in the smooth curve. For example, if the original curve has 100 points, a value of 2.5 will yield a new smoothed curve with 250 points. Ignored if n is specified.


integer; number of vertices in the smoothed curve.


A matrix with the coordinates of the smoothed curve.


This function works on matrices of points and is generally not called directly. Instead, use smooth() with method = "spline" to apply this smoothing algorithm to spatial features.


The spline method was inspired by the following StackExchange answers:

See also


# smooth_spline works on matrices of coordinates # use the matrix of coordinates defining a polygon as an example m <- jagged_polygons$geometry[[2]][[1]] m_smooth <- smooth_spline(m, wrap = TRUE) class(m)
#> [1] "matrix" "array"
#> [1] "matrix" "array"
plot(m_smooth, type = "l", col = "red", axes = FALSE, xlab = NA, ylab = NA)
lines(m, col = "black")
# smooth is a wrapper for smooth_spline that works on spatial features library(sf) p <- jagged_polygons$geometry[[2]] p_smooth <- smooth(p, method = "spline") class(p)
#> [1] "XY" "POLYGON" "sfg"
#> [1] "XY" "POLYGON" "sfg"
plot(p_smooth, border = "red")
plot(p, add = TRUE)